First FDA Approval For Clinical Cloud-Based Deep Learning In Healthcare

The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole.

Arterys’s medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines.

In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on.

Arterys was founded by Fabien Beckers, John Axerio-Cilies, Albert Hsiao and Shreyas Vasanawala when they met at Stanford University with a shared passion for the transformative potential of machine learning.

The current use for their platform – others are planned – is to help physicians understand how a heart is functioning, by providing accurate measurements of the volume of each ventricle allowing more precise assessment of health.

Beckers tells me “This is a huge deal – it’s the first time this new way of imaging has been cleared for clinical application. It’s about truly helping clinical workflow to move into the cloud and deep learning and do something pretty substantial. It opens the seals, and sets a precedent for what can be done.”

Deep Learning

After being fed 1,000 cases as training data, Arterys Cardio DL ran supervised learning algorithms and came up with around 10 million rules based on connections it found within the data. Its purpose is to become able to recognize and identify problems without manual intervention. However, the aim isn’t to replace doctors, Beckers assures me, but to offer them tools which will help them do their jobs more effectively.

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“We’re trying to make it quantitative and data-driven. We started with the heart because that is one of the hardest organs to do – and now we know we can do this, we can use it in many other areas.

“With the heart, the left ventricle is kind of rounded but very straightforward, whereas he right is peanut-shaped and kind of complex. Proving that this technology can be used to analyze images of both is a big deal, because it’s something that’s taking too long using the conventional approach.

“Doing this really shows how dramatically and profoundly these technologies can be helpful.”

The Arterys platform is cloud-based. This is important because it will enable physicians to come together with data from all around the world, which Arterys will continue to teach itself from. With time and enough data, it should be able to one day show that it can not only match humans for accuracy, but beat them.

This brings special challenges of course as the data is highly sensitive and personal.

“Medical imaging is roughly a $3 billion market and it’s workstation based – is there any other industry than healthcare which even still uses the word ‘workstation’?” asks Beckers.

Cloud security

“We tried to understand why the cloud wasn’t embraced in healthcare in the same way as in say fintech, and we found it was this fear of data piracy – you can be Google, GE or any brand in the world but a hospital isn’t going to feel comfortable sending your personal health information (PHI) to the cloud.”

The solution was a system known as PHI Service which enables personal identifying information to be stripped from the imaging data at the point it is collected – generally a hospital. When accredited users of the system – doctors or other medical staff with authority to view personal records – log in, it grabs the imaging data and analytical results from Arterys’s cloud, and the PHI from the hospital’s secure server, and rebuilds it. Arterys itself never receives any information which can be used to identify individuals.

Authenticating systems like this, backed by encryption and secure transfer protocols are likely to play an increasing role in overcoming problems inherent to storing and analyzing personal data. The FDA’s approval for Arterys solution is another important step forward.

The huge potential for learning systems to be applied to the vast amount of digital image data collected by medical scanning equipment has been talked about for some time. But now Arterys has been given 501(k) clearance, the way has been cleared for many more applications of this ground-breaking technology. Arterys itself is already well on the way to producing its next application of its technology – this time, cancer is among the challenges it’s taking aim at. With the FDA approval signalling an ongoing keenness from governments to support the use of AI and machine learning, I think we can expect more breakthroughs soon.

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand ...